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Dynamic Partition Pruning: Before Optimiza
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Classroom Contents
How Apache Spark 3.0 and Delta Lake Enhance Data Lake Reliability
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- 1 Intro
- 2 databricks
- 3 Deep Dive into the New Features of Apache Spark 3.0
- 4 A Delta Lake 0.7.0 + Spark 3.0 AMA
- 5 Spark Catalyst Optimizer
- 6 Adaptive Query Execution AQE
- 7 Apache SparkTM 3.0 AQE Fundamentals
- 8 Starting with Broadcast Hash Joins
- 9 Dynamically Switching Join Strategies Apache Spark 3.0 NE Fundamentals
- 10 Dynamically Coalescing Shuffle Partitions Apache Spark 3.0 ADÉ Fundamentals
- 11 Dynamically Optimize Skew Joins
- 12 TPC-DS performance gains from AQE
- 13 Dynamic Partition Pruning: Before Optimiza
- 14 How to Use Join Hints? Broadcast Hash Join
- 15 Extensibility and Ecosystem
- 16 Data Source V2
- 17 But what happens with DML under the cover What really happens to the file system when you run delete update and merge?
- 18 Time Travel The transaction log and additive files - data versioning
- 19 Control Table History Retention
- 20 Enable DataSourceV2 and Catalog API Integration
- 21 Data Quality Framework Improved SOL DOL and DMLS and ACID Transactions are just the start
- 22 Lakehouse Paradigm Improved Performance. DW-like capabilities, on low cost cloud object stores
- 23 Try out Spark 3.0 + Delta Lake now!
